PinPoint

Dashboard with Sydney Heatmap

NB: The slide deck is attached via the 'additional info' section

Inspiration

Adapting to a new city is difficult for all, Individuals, Institutions, Companies, etc. Companies expanding to new locations or those opening for the first time rely on tedious and inaccurate mathematical predictions and true local intuition is hard to find.

What it does

PinPoint collates every economic, social and geographic indicator that goes into successful spatial decision making. It presents various indicators nation-wide and uses an advanced algorithm to identify the most promising of locations on a heatmap of feasibility.

How we built it

PinPoint was built from the ground-up using a html, css and typescript front end within react. And an efficient back-end algorithm and file handling system in python. The front end was constructed with help of UI library shadcn and the globe rendering library mapbox-gl.

Challenges we ran into

The number of data points processed was in the billions, writing code that was storage and memory efficient as well as expansive enough to accomplish our goals is hardly an easy task. Pulling data from multiple sources and attempting to clean them into one normalised and clean source of truth requires hours of grueling data conversion and cleaning.

Accomplishments that we're proud of

We believe the aesthetic of the heatmap render, appeal of the dashboard, pure scale of the data processing and the accuracy of the data is absolutely profound for one weekend's work.

What we learned

We learned a lot about large data processing, shortcuts for file conversion and downloads, etc. We all feel far more confident in handling large data in python as well as intelligent ways to render this data on a tight computing budget.

What's next for PinPoint

The obvious expansion is improving the backend to cover the remainder of Australia and then moving global. Furthermore, using the foundations of the current implementation PinPoint could function successfully in consumer and personal markets such as real-estate decision making.